Attempts to detect a particular (‘target’) microbial presence or contamination thereby are divided in the art into two broad groups: (i) direct specific detection of the target microbe by determining a presence or absence status for a presumably ‘target microbe-specific’ marker or characteristic; and (ii) indirect detection, based on determining a presence or absence status for a presumably ‘indicator microbe-specific’ marker, which if present is deemed to be indicative of the presence of the target microbe. Such detection schemes, whether direct (target marker) or indirect (indicator microbe marker), have at least two fundamental problems by virtue of being premised on isolated presence/absence tests that yield only an isolated presence/absence signal.
First, a “presence/absence” test is a hypothesis test for which only two possibilities exist with respect to the null hypothesis: either it is true or it is false. In practice, “presence/absence” tests are thus susceptible to two types of errors: type-1 errors (false positives), occurring when the test result is declared positive when the null hypothesis is true (i.e., the condition being tested for does not exist); and type-2 errors (false negatives), occurring when the test result is declared negative when the null hypothesis is false. These errors are the result of non-analytic sampling and analysis errors having a variety of sources including, for example, instances where the test is not sensitive enough to detect a target-specific marker even if present, or where errors are introduced during collecting and/or preparing samples, executing test procedures, or in calculating results. Additionally, a false positive might occur where a presumed ‘target microbe-specific’ marker is not absolutely specific, but is associated with one or more genetically distinct microbes. Because of Type 1 and 2 errors, therefore, a single test cannot always be regarded as a definitive measure of whether the microbial behavior is present or absent.
Second, prior art detection schemes are not effectively applicable to statistical process control (SPC). SPC is currently applied during the manufacture of many materials, and consists of the systematic monitoring of trends in process control data (e.g., corrective actions are applied to bring a process or system back into control when trends indicate that processes are deviating from desired ranges. SPC conveys distinct economic advantages to a manufacturer. By verifying, for example, that conditions during the manufacturing process fall within a range, SPC helps reassure that the quality of the finished product will be acceptable. Additionally, trend information can be used to initiate corrective actions before product characteristics fall out of acceptable ranges, thereby increasing yields of acceptable finished products.
However, for the majority of samples tested by prior art presence/absence detection schemes, the particular ‘target’ or ‘indicator’ microbes are either not present, or are present at undetectable levels, giving rise to numerous isolated negative values that cannot be effectively used in SPC to provide early warning of process failure, exposure and risk assessment, and to facilitate risk based decision making.
For example, manufacturing of food, drinking water, pharmaceuticals and many other materials requires processes and protocols that result in finished goods with low or no microbial burden. Unfortunately, as described above, the ability to apply SPC to microbiological data is often severely limited. A specific case in point relates to the use of generic E. coli ‘count’ data from carcasses for SPC of the beef manufacturing processes in abattoirs. The USDA Food Safety and Inspection Service has encouraged the use of ‘count’ data in this manner. Practically, however, many E. coli count data points fall below the limit of detection in clean/semi-clean environments, and it has become evident that SPC cannot be applied when the majority of the data points do not allow identification of trends.
Equally illustrative are the difficulties faced in attempting to apply trend analysis and SPC to E. coli O157:H7 presence/absence test results generated from “hold and release” testing of beef trim products. Application of trend analysis and SPC to such test results for the purpose of directing meaningful pre-emptive and preventative remedial action is highly desirable, because there are severe adverse economic consequences when a positive (pathogen present) test result is obtained. Practically speaking, however, the incidence rate of positive test results may be very low (ca. 1% for E. coli O157:H7 in beef). Again, it has become evident that SPC cannot be applied when the majority of the data points do not provide positive tangible results that would allow for identification of trends.
Pronounced need in the art. There is, therefore, a pronounced need in the art for more reliable and robust methods of determining whether a particular target microbe, or associated property thereof, is present, or optimally present, in a process or system that is receptive to a plurality of genetically distinct microbes. There is also a pronounced need in the art for methods for predicting a presence of a target microbe, or target microbe associated condition in such processes or systems, and for identifying trends for SPC applications to processes or systems that are receptive to a plurality of genetically distinct microbes (e.g., manufacturing environments) to help ensure that finished product meets quality and yield objectives with respect to microbial burden or distribution.
There is a pronounced need in the art for methods of determining microbial performance potential in a process or system that is receptive to a plurality of genetically distinct microbes (e.g., bioremediation, fermentation, spoliation). There is a pronounced need in the art for methods of predicting microbial performance potential in such processes or systems.
There is a need, therefore, to extract, derive and/or generate additional data from microbial test methods that is suitable for the application in the context of microbial detection, trend analysis and SPC methodologies.